Struct statrs::distribution::Triangular
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pub struct Triangular { /* fields omitted */ }
Implements the Triangular distribution
Examples
use statrs::distribution::{Triangular, Continuous}; use statrs::statistics::Mean; let n = Triangular::new(0.0, 5.0, 2.5).unwrap(); assert_eq!(n.mean(), 7.5 / 3.0); assert_eq!(n.pdf(2.5), 5.0 / 12.5);
Methods
impl Triangular
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fn new(min: f64, max: f64, mode: f64) -> Result<Triangular>
Constructs a new triangular distribution with a minimum of min
,
maximum of max
, and a mode of mode
.
Errors
Returns an error if min
, max
, or mode
are NaN
or ±INF
.
Returns an error if max < mode
, mode < min
, or max == min
.
Examples
use statrs::distribution::Triangular; let mut result = Triangular::new(0.0, 5.0, 2.5); assert!(result.is_ok()); result = Triangular::new(2.5, 1.5, 0.0); assert!(result.is_err());
Trait Implementations
impl Debug for Triangular
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impl Copy for Triangular
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impl Clone for Triangular
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fn clone(&self) -> Triangular
Returns a copy of the value. Read more
fn clone_from(&mut self, source: &Self)
1.0.0
Performs copy-assignment from source
. Read more
impl PartialEq for Triangular
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fn eq(&self, __arg_0: &Triangular) -> bool
This method tests for self
and other
values to be equal, and is used by ==
. Read more
fn ne(&self, __arg_0: &Triangular) -> bool
This method tests for !=
.
impl Sample<f64> for Triangular
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fn sample<R: Rng>(&mut self, r: &mut R) -> f64
Generate a random sample from a triangular distribution
distribution using r
as the source of randomness.
Refer here for implementation details
impl IndependentSample<f64> for Triangular
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fn ind_sample<R: Rng>(&self, r: &mut R) -> f64
Generate a random independent sample from a triangular distribution
distribution using r
as the source of randomness.
Refer here for implementation details
impl Distribution<f64> for Triangular
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fn sample<R: Rng>(&self, r: &mut R) -> f64
Generate a random sample from a triangular distribution using
r
as the source of randomness.
Examples
use rand::StdRng; use statrs::distribution::{Triangular, Distribution}; let mut r = rand::StdRng::new().unwrap(); let n = Triangular::new(0.0, 5.0, 2.5).unwrap(); print!("{}", n.sample::<StdRng>(&mut r));
impl Univariate<f64, f64> for Triangular
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fn cdf(&self, x: f64) -> f64
Calculates the cumulative distribution function for the triangular distribution
at x
Formula
if x <= min { 0 } if min < x <= mode { (x - min)^2 / ((max - min) * (mode - min)) } else if mode < x < max { 1 - (max - min)^2 / ((max - min) * (max - mode)) } else { 1 }
impl Min<f64> for Triangular
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fn min(&self) -> f64
Returns the minimum value in the domain of the triangular distribution representable by a double precision float
Remarks
The return value is the same min used to construct the distribution
impl Max<f64> for Triangular
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fn max(&self) -> f64
Returns the maximum value in the domain of the triangular distribution representable by a double precision float
Remarks
The return value is the same max used to construct the distribution
impl Mean<f64> for Triangular
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impl Variance<f64> for Triangular
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fn variance(&self) -> f64
Returns the variance of the triangular distribution
Formula
(min^2 + max^2 + mode^2 - min * max - min * mode - max * mode) / 18
fn std_dev(&self) -> f64
Returns the standard deviation of the triangular distribution
Formula
sqrt((min^2 + max^2 + mode^2 - min * max - min * mode - max * mode) / 18)
impl Entropy<f64> for Triangular
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impl Skewness<f64> for Triangular
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fn skewness(&self) -> f64
Returns the skewness of the triangular distribution
Formula
(sqrt(2) * (min + max - 2 * mode) * (2 * min - max - mode) * (min - 2 * max + mode)) / ( 5 * (min^2 + max^2 + mode^2 - min * max - min * mode - max * mode)^(3 / 2))
impl Median<f64> for Triangular
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fn median(&self) -> f64
Returns the median of the triangular distribution
Formula
if mode >= (min + max) / 2 { min + sqrt((max - min) * (mode - min) / 2) } else { max - sqrt((max - min) * (max - mode) / 2) }
impl Mode<f64> for Triangular
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impl Continuous<f64, f64> for Triangular
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fn pdf(&self, x: f64) -> f64
Calculates the probability density function for the triangular distribution
at x
Formula
if x < min { 0 } else if min <= x <= mode { 2 * (x - min) / ((max - min) * (mode - min)) } else if mode < x <= max { 2 * (max - x) / ((max - min) * (max - mode)) } else { 0 }
fn ln_pdf(&self, x: f64) -> f64
Calculates the log probability density function for the triangular distribution
at x
Formula
ln( if x < min { 0 } else if min <= x <= mode { 2 * (x - min) / ((max - min) * (mode - min)) } else if mode < x <= max { 2 * (max - x) / ((max - min) * (max - mode)) } else { 0 } )